Overview

Dataset statistics

Number of variables84
Number of observations30000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 MiB
Average record size in memory189.0 B

Variable types

Numeric15
Categorical69

Warnings

education_5 has constant value "0" Constant
education_6 has constant value "0" Constant
bill_amt1 is highly correlated with bill_amt2 and 4 other fieldsHigh correlation
bill_amt2 is highly correlated with bill_amt1 and 4 other fieldsHigh correlation
bill_amt3 is highly correlated with bill_amt1 and 4 other fieldsHigh correlation
bill_amt4 is highly correlated with bill_amt1 and 4 other fieldsHigh correlation
bill_amt5 is highly correlated with bill_amt1 and 4 other fieldsHigh correlation
bill_amt6 is highly correlated with bill_amt1 and 4 other fieldsHigh correlation
pay_1_-1 is highly correlated with pay_2_-1 and 2 other fieldsHigh correlation
pay_1_0 is highly correlated with pay_2_0 and 3 other fieldsHigh correlation
pay_1_6 is highly correlated with pay_2_5High correlation
pay_1_7 is highly correlated with pay_2_6 and 1 other fieldsHigh correlation
pay_1_8 is highly correlated with pay_2_7 and 2 other fieldsHigh correlation
pay_2_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_2_0 is highly correlated with pay_1_0 and 5 other fieldsHigh correlation
pay_2_2 is highly correlated with pay_3_2High correlation
pay_2_4 is highly correlated with pay_3_3High correlation
pay_2_5 is highly correlated with pay_1_6 and 1 other fieldsHigh correlation
pay_2_6 is highly correlated with pay_1_7 and 1 other fieldsHigh correlation
pay_2_7 is highly correlated with pay_1_8 and 2 other fieldsHigh correlation
pay_3_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_3_0 is highly correlated with pay_1_0 and 5 other fieldsHigh correlation
pay_3_1 is highly correlated with pay_4_1High correlation
pay_3_2 is highly correlated with pay_2_2 and 1 other fieldsHigh correlation
pay_3_3 is highly correlated with pay_2_4High correlation
pay_3_4 is highly correlated with pay_2_5High correlation
pay_3_5 is highly correlated with pay_1_7 and 1 other fieldsHigh correlation
pay_3_6 is highly correlated with pay_1_8 and 2 other fieldsHigh correlation
pay_3_7 is highly correlated with pay_4_7 and 2 other fieldsHigh correlation
pay_3_8 is highly correlated with pay_5_8High correlation
pay_4_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_4_0 is highly correlated with pay_1_0 and 5 other fieldsHigh correlation
pay_4_1 is highly correlated with pay_3_1High correlation
pay_4_2 is highly correlated with pay_3_2 and 1 other fieldsHigh correlation
pay_4_5 is highly correlated with pay_1_8 and 3 other fieldsHigh correlation
pay_4_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_4_8 is highly correlated with pay_5_8High correlation
pay_5_-1 is highly correlated with pay_2_-1 and 4 other fieldsHigh correlation
pay_5_0 is highly correlated with pay_1_0 and 5 other fieldsHigh correlation
pay_5_2 is highly correlated with pay_4_2 and 1 other fieldsHigh correlation
pay_5_4 is highly correlated with pay_4_5High correlation
pay_5_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_5_8 is highly correlated with pay_3_8 and 2 other fieldsHigh correlation
pay_6_-1 is highly correlated with pay_2_-1 and 4 other fieldsHigh correlation
pay_6_0 is highly correlated with pay_2_0 and 4 other fieldsHigh correlation
pay_6_2 is highly correlated with pay_5_2High correlation
pay_6_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_6_8 is highly correlated with pay_5_8High correlation
education_1 is highly correlated with education_2High correlation
education_2 is highly correlated with education_1High correlation
marriage_1 is highly correlated with marriage_2High correlation
marriage_2 is highly correlated with marriage_1High correlation
bill_amt1 is highly correlated with bill_amt2 and 11 other fieldsHigh correlation
bill_amt2 is highly correlated with bill_amt1 and 11 other fieldsHigh correlation
bill_amt3 is highly correlated with bill_amt1 and 11 other fieldsHigh correlation
bill_amt4 is highly correlated with bill_amt1 and 13 other fieldsHigh correlation
bill_amt5 is highly correlated with bill_amt1 and 12 other fieldsHigh correlation
bill_amt6 is highly correlated with bill_amt1 and 9 other fieldsHigh correlation
pay_amt1 is highly correlated with bill_amt1 and 5 other fieldsHigh correlation
pay_amt2 is highly correlated with bill_amt3 and 5 other fieldsHigh correlation
pay_amt3 is highly correlated with bill_amt4 and 7 other fieldsHigh correlation
pay_amt4 is highly correlated with bill_amt4 and 6 other fieldsHigh correlation
pay_amt5 is highly correlated with bill_amt4 and 5 other fieldsHigh correlation
pay_amt6 is highly correlated with bill_amt5 and 4 other fieldsHigh correlation
pay_1_-1 is highly correlated with pay_2_-1 and 2 other fieldsHigh correlation
pay_1_0 is highly correlated with bill_amt1 and 5 other fieldsHigh correlation
pay_1_6 is highly correlated with pay_2_5High correlation
pay_1_7 is highly correlated with pay_2_6 and 1 other fieldsHigh correlation
pay_1_8 is highly correlated with pay_2_7 and 2 other fieldsHigh correlation
pay_2_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_2_0 is highly correlated with bill_amt1 and 8 other fieldsHigh correlation
pay_2_2 is highly correlated with pay_3_2High correlation
pay_2_4 is highly correlated with pay_3_3High correlation
pay_2_5 is highly correlated with pay_1_6 and 1 other fieldsHigh correlation
pay_2_6 is highly correlated with pay_1_7 and 1 other fieldsHigh correlation
pay_2_7 is highly correlated with pay_1_8 and 2 other fieldsHigh correlation
pay_3_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_3_0 is highly correlated with bill_amt1 and 9 other fieldsHigh correlation
pay_3_1 is highly correlated with pay_4_1High correlation
pay_3_2 is highly correlated with pay_2_2 and 1 other fieldsHigh correlation
pay_3_3 is highly correlated with pay_2_4High correlation
pay_3_4 is highly correlated with pay_2_5High correlation
pay_3_5 is highly correlated with pay_1_7 and 1 other fieldsHigh correlation
pay_3_6 is highly correlated with pay_1_8 and 2 other fieldsHigh correlation
pay_3_7 is highly correlated with pay_4_7 and 2 other fieldsHigh correlation
pay_3_8 is highly correlated with pay_5_8High correlation
pay_4_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_4_0 is highly correlated with bill_amt1 and 10 other fieldsHigh correlation
pay_4_1 is highly correlated with pay_3_1High correlation
pay_4_2 is highly correlated with pay_3_2 and 1 other fieldsHigh correlation
pay_4_5 is highly correlated with pay_1_8 and 3 other fieldsHigh correlation
pay_4_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_4_8 is highly correlated with pay_5_8High correlation
pay_5_-1 is highly correlated with pay_2_-1 and 4 other fieldsHigh correlation
pay_5_0 is highly correlated with bill_amt1 and 10 other fieldsHigh correlation
pay_5_2 is highly correlated with pay_4_2 and 1 other fieldsHigh correlation
pay_5_4 is highly correlated with pay_4_5High correlation
pay_5_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_5_8 is highly correlated with pay_3_8 and 2 other fieldsHigh correlation
pay_6_-1 is highly correlated with pay_2_-1 and 4 other fieldsHigh correlation
pay_6_0 is highly correlated with bill_amt1 and 10 other fieldsHigh correlation
pay_6_2 is highly correlated with pay_5_2High correlation
pay_6_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_6_8 is highly correlated with pay_5_8High correlation
education_1 is highly correlated with education_2High correlation
education_2 is highly correlated with education_1High correlation
marriage_1 is highly correlated with marriage_2High correlation
marriage_2 is highly correlated with marriage_1High correlation
bill_amt1 is highly correlated with bill_amt2 and 4 other fieldsHigh correlation
bill_amt2 is highly correlated with bill_amt1 and 6 other fieldsHigh correlation
bill_amt3 is highly correlated with bill_amt1 and 6 other fieldsHigh correlation
bill_amt4 is highly correlated with bill_amt1 and 4 other fieldsHigh correlation
bill_amt5 is highly correlated with bill_amt1 and 5 other fieldsHigh correlation
bill_amt6 is highly correlated with bill_amt1 and 5 other fieldsHigh correlation
pay_amt1 is highly correlated with bill_amt2High correlation
pay_amt2 is highly correlated with bill_amt3High correlation
pay_amt4 is highly correlated with bill_amt5High correlation
pay_amt5 is highly correlated with bill_amt6High correlation
pay_1_-1 is highly correlated with pay_2_-1 and 2 other fieldsHigh correlation
pay_1_0 is highly correlated with pay_2_0 and 3 other fieldsHigh correlation
pay_1_6 is highly correlated with pay_2_5High correlation
pay_1_7 is highly correlated with pay_2_6 and 1 other fieldsHigh correlation
pay_1_8 is highly correlated with pay_2_7 and 2 other fieldsHigh correlation
pay_2_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_2_0 is highly correlated with pay_1_0 and 5 other fieldsHigh correlation
pay_2_2 is highly correlated with pay_3_2High correlation
pay_2_4 is highly correlated with pay_3_3High correlation
pay_2_5 is highly correlated with pay_1_6 and 1 other fieldsHigh correlation
pay_2_6 is highly correlated with pay_1_7 and 1 other fieldsHigh correlation
pay_2_7 is highly correlated with pay_1_8 and 2 other fieldsHigh correlation
pay_3_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_3_0 is highly correlated with bill_amt2 and 6 other fieldsHigh correlation
pay_3_1 is highly correlated with pay_4_1High correlation
pay_3_2 is highly correlated with pay_2_2 and 1 other fieldsHigh correlation
pay_3_3 is highly correlated with pay_2_4High correlation
pay_3_4 is highly correlated with pay_2_5High correlation
pay_3_5 is highly correlated with pay_1_7 and 1 other fieldsHigh correlation
pay_3_6 is highly correlated with pay_1_8 and 2 other fieldsHigh correlation
pay_3_7 is highly correlated with pay_4_7 and 2 other fieldsHigh correlation
pay_3_8 is highly correlated with pay_5_8High correlation
pay_4_-1 is highly correlated with pay_1_-1 and 5 other fieldsHigh correlation
pay_4_0 is highly correlated with bill_amt3 and 6 other fieldsHigh correlation
pay_4_1 is highly correlated with pay_3_1High correlation
pay_4_2 is highly correlated with pay_3_2 and 1 other fieldsHigh correlation
pay_4_5 is highly correlated with pay_1_8 and 3 other fieldsHigh correlation
pay_4_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_4_8 is highly correlated with pay_5_8High correlation
pay_5_-1 is highly correlated with pay_2_-1 and 4 other fieldsHigh correlation
pay_5_0 is highly correlated with pay_1_0 and 5 other fieldsHigh correlation
pay_5_2 is highly correlated with pay_4_2 and 1 other fieldsHigh correlation
pay_5_4 is highly correlated with pay_4_5High correlation
pay_5_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_5_8 is highly correlated with pay_3_8 and 2 other fieldsHigh correlation
pay_6_-1 is highly correlated with pay_2_-1 and 4 other fieldsHigh correlation
pay_6_0 is highly correlated with pay_2_0 and 4 other fieldsHigh correlation
pay_6_2 is highly correlated with pay_5_2High correlation
pay_6_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_6_8 is highly correlated with pay_5_8High correlation
education_1 is highly correlated with education_2High correlation
education_2 is highly correlated with education_1High correlation
marriage_1 is highly correlated with marriage_2High correlation
marriage_2 is highly correlated with marriage_1High correlation
limit_bal is highly correlated with bill_amt2 and 5 other fieldsHigh correlation
pay_4_8 is highly correlated with pay_2_8 and 1 other fieldsHigh correlation
pay_3_3 is highly correlated with pay_2_4High correlation
pay_2_5 is highly correlated with pay_1_6 and 1 other fieldsHigh correlation
bill_amt3 is highly correlated with bill_amt2 and 6 other fieldsHigh correlation
education_3 is highly correlated with education_2High correlation
pay_2_8 is highly correlated with pay_4_8 and 3 other fieldsHigh correlation
pay_amt4 is highly correlated with pay_amt3 and 1 other fieldsHigh correlation
pay_3_6 is highly correlated with pay_1_8 and 3 other fieldsHigh correlation
pay_4_1 is highly correlated with pay_2_8 and 2 other fieldsHigh correlation
pay_1_7 is highly correlated with pay_2_6 and 2 other fieldsHigh correlation
pay_3_1 is highly correlated with pay_4_1High correlation
pay_6_4 is highly correlated with pay_4_4 and 2 other fieldsHigh correlation
pay_3_-1 is highly correlated with pay_1_-1 and 8 other fieldsHigh correlation
pay_1_-1 is highly correlated with pay_3_-1 and 7 other fieldsHigh correlation
pay_2_6 is highly correlated with pay_1_7 and 2 other fieldsHigh correlation
education_1 is highly correlated with education_2High correlation
pay_6_8 is highly correlated with pay_2_8 and 1 other fieldsHigh correlation
pay_1_0 is highly correlated with pay_3_-1 and 9 other fieldsHigh correlation
pay_5_8 is highly correlated with pay_4_8 and 3 other fieldsHigh correlation
pay_6_0 is highly correlated with pay_1_0 and 8 other fieldsHigh correlation
bill_amt2 is highly correlated with limit_bal and 6 other fieldsHigh correlation
pay_1_6 is highly correlated with pay_2_5 and 1 other fieldsHigh correlation
marriage_1 is highly correlated with marriage_2 and 1 other fieldsHigh correlation
pay_2_0 is highly correlated with pay_3_-1 and 10 other fieldsHigh correlation
default_payment_next_month is highly correlated with pay_1_2High correlation
pay_1_5 is highly correlated with pay_2_4High correlation
pay_6_5 is highly correlated with pay_5_6High correlation
pay_4_0 is highly correlated with pay_3_-1 and 9 other fieldsHigh correlation
pay_6_-1 is highly correlated with pay_3_-1 and 6 other fieldsHigh correlation
pay_2_4 is highly correlated with pay_3_3 and 1 other fieldsHigh correlation
pay_4_3 is highly correlated with pay_3_4High correlation
pay_1_8 is highly correlated with pay_3_6 and 3 other fieldsHigh correlation
pay_4_4 is highly correlated with pay_1_7 and 5 other fieldsHigh correlation
marriage_2 is highly correlated with marriage_1 and 1 other fieldsHigh correlation
bill_amt6 is highly correlated with limit_bal and 6 other fieldsHigh correlation
pay_amt3 is highly correlated with limit_bal and 8 other fieldsHigh correlation
pay_5_3 is highly correlated with pay_4_4High correlation
pay_1_1 is highly correlated with pay_1_0 and 2 other fieldsHigh correlation
bill_amt5 is highly correlated with limit_bal and 6 other fieldsHigh correlation
pay_3_2 is highly correlated with pay_4_2 and 4 other fieldsHigh correlation
pay_amt1 is highly correlated with pay_amt4 and 2 other fieldsHigh correlation
pay_5_-1 is highly correlated with pay_3_-1 and 8 other fieldsHigh correlation
pay_3_5 is highly correlated with pay_1_7 and 2 other fieldsHigh correlation
pay_5_6 is highly correlated with pay_6_5High correlation
pay_4_2 is highly correlated with pay_4_0 and 4 other fieldsHigh correlation
age is highly correlated with marriage_1 and 1 other fieldsHigh correlation
bill_amt4 is highly correlated with limit_bal and 8 other fieldsHigh correlation
pay_4_5 is highly correlated with pay_3_6 and 4 other fieldsHigh correlation
pay_3_7 is highly correlated with pay_5_7 and 2 other fieldsHigh correlation
pay_5_4 is highly correlated with pay_3_6 and 5 other fieldsHigh correlation
pay_5_0 is highly correlated with pay_1_0 and 9 other fieldsHigh correlation
bill_amt1 is highly correlated with limit_bal and 6 other fieldsHigh correlation
pay_2_-1 is highly correlated with pay_3_-1 and 8 other fieldsHigh correlation
pay_5_2 is highly correlated with pay_3_2 and 4 other fieldsHigh correlation
education_2 is highly correlated with education_3 and 1 other fieldsHigh correlation
pay_2_2 is highly correlated with pay_1_0 and 6 other fieldsHigh correlation
pay_1_2 is highly correlated with default_payment_next_month and 1 other fieldsHigh correlation
pay_amt5 is highly correlated with bill_amt3 and 1 other fieldsHigh correlation
pay_1_4 is highly correlated with pay_2_3High correlation
pay_3_4 is highly correlated with pay_2_5 and 3 other fieldsHigh correlation
pay_5_5 is highly correlated with pay_6_4High correlation
pay_amt2 is highly correlated with bill_amt3 and 3 other fieldsHigh correlation
pay_4_-1 is highly correlated with pay_3_-1 and 8 other fieldsHigh correlation
pay_2_3 is highly correlated with pay_1_4High correlation
pay_5_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_6_3 is highly correlated with pay_4_5 and 1 other fieldsHigh correlation
pay_4_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
pay_2_7 is highly correlated with pay_3_6 and 3 other fieldsHigh correlation
pay_3_0 is highly correlated with pay_3_-1 and 10 other fieldsHigh correlation
pay_6_2 is highly correlated with pay_6_0 and 3 other fieldsHigh correlation
pay_6_7 is highly correlated with pay_3_7 and 2 other fieldsHigh correlation
education_5 is highly correlated with pay_4_8 and 67 other fieldsHigh correlation
pay_4_8 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_3_3 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_2_5 is highly correlated with education_5 and 3 other fieldsHigh correlation
education_3 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_2_8 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_2_1 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_3_5 is highly correlated with education_5 and 3 other fieldsHigh correlation
pay_5_6 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_3_6 is highly correlated with education_5 and 4 other fieldsHigh correlation
pay_4_1 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_1_7 is highly correlated with education_5 and 3 other fieldsHigh correlation
pay_3_1 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_6_4 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_4_2 is highly correlated with education_5 and 3 other fieldsHigh correlation
female is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_3_-1 is highly correlated with education_5 and 7 other fieldsHigh correlation
pay_1_-1 is highly correlated with education_5 and 4 other fieldsHigh correlation
pay_2_6 is highly correlated with education_5 and 3 other fieldsHigh correlation
education_1 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_6_8 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_1_0 is highly correlated with education_5 and 5 other fieldsHigh correlation
pay_4_5 is highly correlated with education_5 and 5 other fieldsHigh correlation
pay_3_7 is highly correlated with education_5 and 4 other fieldsHigh correlation
pay_5_8 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_6_0 is highly correlated with education_5 and 6 other fieldsHigh correlation
pay_5_4 is highly correlated with education_5 and 2 other fieldsHigh correlation
marriage_3 is highly correlated with education_5 and 1 other fieldsHigh correlation
education_6 is highly correlated with education_5 and 67 other fieldsHigh correlation
pay_5_0 is highly correlated with education_5 and 7 other fieldsHigh correlation
pay_1_6 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_2_-1 is highly correlated with education_5 and 7 other fieldsHigh correlation
pay_5_2 is highly correlated with education_5 and 3 other fieldsHigh correlation
marriage_1 is highly correlated with education_5 and 2 other fieldsHigh correlation
education_2 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_2_0 is highly correlated with education_5 and 7 other fieldsHigh correlation
pay_2_2 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_1_2 is highly correlated with education_5 and 1 other fieldsHigh correlation
default_payment_next_month is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_1_5 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_1_4 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_4_0 is highly correlated with education_5 and 7 other fieldsHigh correlation
pay_6_5 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_3_4 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_5_5 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_4_-1 is highly correlated with education_5 and 7 other fieldsHigh correlation
pay_2_3 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_5_7 is highly correlated with education_5 and 4 other fieldsHigh correlation
pay_6_-1 is highly correlated with education_5 and 6 other fieldsHigh correlation
pay_1_3 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_2_4 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_5_3 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_4_7 is highly correlated with education_5 and 4 other fieldsHigh correlation
pay_6_3 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_6_6 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_4_3 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_2_7 is highly correlated with education_5 and 4 other fieldsHigh correlation
pay_4_6 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_3_0 is highly correlated with education_5 and 7 other fieldsHigh correlation
pay_1_8 is highly correlated with education_5 and 4 other fieldsHigh correlation
pay_3_8 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_4_4 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_6_2 is highly correlated with education_5 and 2 other fieldsHigh correlation
marriage_2 is highly correlated with education_5 and 2 other fieldsHigh correlation
pay_1_1 is highly correlated with education_5 and 1 other fieldsHigh correlation
education_4 is highly correlated with education_5 and 1 other fieldsHigh correlation
pay_6_7 is highly correlated with education_5 and 4 other fieldsHigh correlation
pay_5_-1 is highly correlated with education_5 and 6 other fieldsHigh correlation
pay_3_2 is highly correlated with education_5 and 3 other fieldsHigh correlation
pay_amt2 is highly skewed (γ1 = 30.45381745) Skewed
id is uniformly distributed Uniform
id has unique values Unique
bill_amt1 has 2008 (6.7%) zeros Zeros
bill_amt2 has 2506 (8.4%) zeros Zeros
bill_amt3 has 2870 (9.6%) zeros Zeros
bill_amt4 has 3195 (10.7%) zeros Zeros
bill_amt5 has 3506 (11.7%) zeros Zeros
bill_amt6 has 4020 (13.4%) zeros Zeros
pay_amt1 has 5249 (17.5%) zeros Zeros
pay_amt2 has 5396 (18.0%) zeros Zeros
pay_amt3 has 5968 (19.9%) zeros Zeros
pay_amt4 has 6408 (21.4%) zeros Zeros
pay_amt5 has 6703 (22.3%) zeros Zeros
pay_amt6 has 7173 (23.9%) zeros Zeros

Reproduction

Analysis started2021-11-13 01:06:32.714160
Analysis finished2021-11-13 01:08:23.125936
Duration1 minute and 50.41 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct30000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15000.5
Minimum1
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-12T19:08:23.243926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1500.95
Q17500.75
median15000.5
Q322500.25
95-th percentile28500.05
Maximum30000
Range29999
Interquartile range (IQR)14999.5

Descriptive statistics

Standard deviation8660.398374
Coefficient of variation (CV)0.5773406469
Kurtosis-1.2
Mean15000.5
Median Absolute Deviation (MAD)7500
Skewness0
Sum450015000
Variance75002500
MonotonicityStrictly increasing
2021-11-12T19:08:23.455906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
13221
 
< 0.1%
156291
 
< 0.1%
94861
 
< 0.1%
115351
 
< 0.1%
217921
 
< 0.1%
238411
 
< 0.1%
176981
 
< 0.1%
197471
 
< 0.1%
299881
 
< 0.1%
Other values (29990)29990
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
300001
< 0.1%
299991
< 0.1%
299981
< 0.1%
299971
< 0.1%
299961
< 0.1%
299951
< 0.1%
299941
< 0.1%
299931
< 0.1%
299921
< 0.1%
299911
< 0.1%

limit_bal
Real number (ℝ≥0)

HIGH CORRELATION

Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167484.3227
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-12T19:08:23.732863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum1000000
Range990000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129747.6616
Coefficient of variation (CV)0.7746854124
Kurtosis0.5362628964
Mean167484.3227
Median Absolute Deviation (MAD)90000
Skewness0.9928669605
Sum5024529680
Variance1.683445568 × 1010
MonotonicityNot monotonic
2021-11-12T19:08:24.023832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500003365
 
11.2%
200001976
 
6.6%
300001610
 
5.4%
800001567
 
5.2%
2000001528
 
5.1%
1500001110
 
3.7%
1000001048
 
3.5%
180000995
 
3.3%
360000881
 
2.9%
60000825
 
2.8%
Other values (71)15095
50.3%
ValueCountFrequency (%)
10000493
 
1.6%
160002
 
< 0.1%
200001976
6.6%
300001610
5.4%
40000230
 
0.8%
500003365
11.2%
60000825
 
2.8%
70000731
 
2.4%
800001567
5.2%
90000651
 
2.2%
ValueCountFrequency (%)
10000001
 
< 0.1%
8000002
 
< 0.1%
7800002
 
< 0.1%
7600001
 
< 0.1%
7500004
< 0.1%
7400002
 
< 0.1%
7300002
 
< 0.1%
7200003
 
< 0.1%
7100006
< 0.1%
7000008
< 0.1%

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.4855
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-12T19:08:24.297794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.217904068
Coefficient of variation (CV)0.2597653709
Kurtosis0.04430337824
Mean35.4855
Median Absolute Deviation (MAD)6
Skewness0.7322458688
Sum1064565
Variance84.96975541
MonotonicityNot monotonic
2021-11-12T19:08:24.561763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291605
 
5.3%
271477
 
4.9%
281409
 
4.7%
301395
 
4.7%
261256
 
4.2%
311217
 
4.1%
251186
 
4.0%
341162
 
3.9%
321158
 
3.9%
331146
 
3.8%
Other values (46)16989
56.6%
ValueCountFrequency (%)
2167
 
0.2%
22560
 
1.9%
23931
3.1%
241127
3.8%
251186
4.0%
261256
4.2%
271477
4.9%
281409
4.7%
291605
5.3%
301395
4.7%
ValueCountFrequency (%)
791
 
< 0.1%
753
 
< 0.1%
741
 
< 0.1%
734
 
< 0.1%
723
 
< 0.1%
713
 
< 0.1%
7010
< 0.1%
6915
0.1%
685
 
< 0.1%
6716
0.1%

bill_amt1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22723
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51223.3309
Minimum-165580
Maximum964511
Zeros2008
Zeros (%)6.7%
Negative590
Negative (%)2.0%
Memory size234.5 KiB
2021-11-12T19:08:24.849730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-165580
5-th percentile0
Q13558.75
median22381.5
Q367091
95-th percentile201203.05
Maximum964511
Range1130091
Interquartile range (IQR)63532.25

Descriptive statistics

Standard deviation73635.86058
Coefficient of variation (CV)1.437545339
Kurtosis9.806289341
Mean51223.3309
Median Absolute Deviation (MAD)21800.5
Skewness2.663861022
Sum1536699927
Variance5422239963
MonotonicityNot monotonic
2021-11-12T19:08:25.123702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02008
 
6.7%
390244
 
0.8%
78076
 
0.3%
32672
 
0.2%
31663
 
0.2%
250059
 
0.2%
39649
 
0.2%
240039
 
0.1%
41629
 
0.1%
105025
 
0.1%
Other values (22713)27336
91.1%
ValueCountFrequency (%)
-1655801
< 0.1%
-1549731
< 0.1%
-153081
< 0.1%
-143861
< 0.1%
-115451
< 0.1%
-106821
< 0.1%
-98021
< 0.1%
-90951
< 0.1%
-81871
< 0.1%
-74381
< 0.1%
ValueCountFrequency (%)
9645111
< 0.1%
7468141
< 0.1%
6530621
< 0.1%
6304581
< 0.1%
6266481
< 0.1%
6217491
< 0.1%
6138601
< 0.1%
6107231
< 0.1%
6085941
< 0.1%
6040191
< 0.1%

bill_amt2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22346
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49179.07517
Minimum-69777
Maximum983931
Zeros2506
Zeros (%)8.4%
Negative669
Negative (%)2.2%
Memory size234.5 KiB
2021-11-12T19:08:25.393668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-69777
5-th percentile0
Q12984.75
median21200
Q364006.25
95-th percentile194792.2
Maximum983931
Range1053708
Interquartile range (IQR)61021.5

Descriptive statistics

Standard deviation71173.76878
Coefficient of variation (CV)1.447236829
Kurtosis10.30294592
Mean49179.07517
Median Absolute Deviation (MAD)20810
Skewness2.705220853
Sum1475372255
Variance5065705363
MonotonicityNot monotonic
2021-11-12T19:08:25.621642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02506
 
8.4%
390231
 
0.8%
78075
 
0.2%
32675
 
0.2%
31672
 
0.2%
250051
 
0.2%
39651
 
0.2%
240042
 
0.1%
-20029
 
0.1%
41628
 
0.1%
Other values (22336)26840
89.5%
ValueCountFrequency (%)
-697771
< 0.1%
-675261
< 0.1%
-333501
< 0.1%
-300001
< 0.1%
-262141
< 0.1%
-247041
< 0.1%
-247021
< 0.1%
-229601
< 0.1%
-186181
< 0.1%
-180881
< 0.1%
ValueCountFrequency (%)
9839311
< 0.1%
7439701
< 0.1%
6715631
< 0.1%
6467701
< 0.1%
6244751
< 0.1%
6059431
< 0.1%
5977931
< 0.1%
5868251
< 0.1%
5817751
< 0.1%
5776811
< 0.1%

bill_amt3
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22026
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47013.1548
Minimum-157264
Maximum1664089
Zeros2870
Zeros (%)9.6%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2021-11-12T19:08:25.872612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-157264
5-th percentile0
Q12666.25
median20088.5
Q360164.75
95-th percentile187821.05
Maximum1664089
Range1821353
Interquartile range (IQR)57498.5

Descriptive statistics

Standard deviation69349.38743
Coefficient of variation (CV)1.475106015
Kurtosis19.78325514
Mean47013.1548
Median Absolute Deviation (MAD)19708.5
Skewness3.087830046
Sum1410394644
Variance4809337537
MonotonicityNot monotonic
2021-11-12T19:08:26.089582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02870
 
9.6%
390275
 
0.9%
78074
 
0.2%
32663
 
0.2%
31662
 
0.2%
39648
 
0.2%
250040
 
0.1%
240039
 
0.1%
41629
 
0.1%
20027
 
0.1%
Other values (22016)26473
88.2%
ValueCountFrequency (%)
-1572641
< 0.1%
-615061
< 0.1%
-461271
< 0.1%
-340411
< 0.1%
-254431
< 0.1%
-247021
< 0.1%
-203201
< 0.1%
-177061
< 0.1%
-159101
< 0.1%
-156411
< 0.1%
ValueCountFrequency (%)
16640891
< 0.1%
8550861
< 0.1%
6931311
< 0.1%
6896431
< 0.1%
6896271
< 0.1%
6320411
< 0.1%
5974151
< 0.1%
5789711
< 0.1%
5779571
< 0.1%
5770151
< 0.1%

bill_amt4
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21548
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43262.94897
Minimum-170000
Maximum891586
Zeros3195
Zeros (%)10.7%
Negative675
Negative (%)2.2%
Memory size234.5 KiB
2021-11-12T19:08:26.314586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-170000
5-th percentile0
Q12326.75
median19052
Q354506
95-th percentile174333.35
Maximum891586
Range1061586
Interquartile range (IQR)52179.25

Descriptive statistics

Standard deviation64332.85613
Coefficient of variation (CV)1.487019671
Kurtosis11.30932483
Mean43262.94897
Median Absolute Deviation (MAD)18656
Skewness2.821965291
Sum1297888469
Variance4138716378
MonotonicityNot monotonic
2021-11-12T19:08:26.505530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03195
 
10.7%
390246
 
0.8%
780101
 
0.3%
31668
 
0.2%
32662
 
0.2%
39644
 
0.1%
15039
 
0.1%
240039
 
0.1%
250034
 
0.1%
41633
 
0.1%
Other values (21538)26139
87.1%
ValueCountFrequency (%)
-1700001
< 0.1%
-813341
< 0.1%
-651671
< 0.1%
-506161
< 0.1%
-466271
< 0.1%
-345031
< 0.1%
-274901
< 0.1%
-243031
< 0.1%
-221081
< 0.1%
-203201
< 0.1%
ValueCountFrequency (%)
8915861
< 0.1%
7068641
< 0.1%
6286991
< 0.1%
6168361
< 0.1%
5728051
< 0.1%
5690341
< 0.1%
5656691
< 0.1%
5635431
< 0.1%
5480201
< 0.1%
5426531
< 0.1%

bill_amt5
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21010
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40311.40097
Minimum-81334
Maximum927171
Zeros3506
Zeros (%)11.7%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2021-11-12T19:08:26.708508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-81334
5-th percentile0
Q11763
median18104.5
Q350190.5
95-th percentile165794.3
Maximum927171
Range1008505
Interquartile range (IQR)48427.5

Descriptive statistics

Standard deviation60797.15577
Coefficient of variation (CV)1.508187617
Kurtosis12.30588129
Mean40311.40097
Median Absolute Deviation (MAD)17688.5
Skewness2.876379867
Sum1209342029
Variance3696294150
MonotonicityNot monotonic
2021-11-12T19:08:26.895484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03506
 
11.7%
390235
 
0.8%
78094
 
0.3%
31679
 
0.3%
32662
 
0.2%
15058
 
0.2%
39647
 
0.2%
240039
 
0.1%
250037
 
0.1%
41636
 
0.1%
Other values (21000)25807
86.0%
ValueCountFrequency (%)
-813341
< 0.1%
-613721
< 0.1%
-530071
< 0.1%
-466271
< 0.1%
-375941
< 0.1%
-361561
< 0.1%
-304811
< 0.1%
-283351
< 0.1%
-230031
< 0.1%
-207531
< 0.1%
ValueCountFrequency (%)
9271711
< 0.1%
8235401
< 0.1%
5870671
< 0.1%
5517021
< 0.1%
5478801
< 0.1%
5306721
< 0.1%
5243151
< 0.1%
5161391
< 0.1%
5141141
< 0.1%
5082131
< 0.1%

bill_amt6
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20604
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38871.7604
Minimum-339603
Maximum961664
Zeros4020
Zeros (%)13.4%
Negative688
Negative (%)2.3%
Memory size234.5 KiB
2021-11-12T19:08:27.083496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q11256
median17071
Q349198.25
95-th percentile161912
Maximum961664
Range1301267
Interquartile range (IQR)47942.25

Descriptive statistics

Standard deviation59554.10754
Coefficient of variation (CV)1.53206613
Kurtosis12.27070529
Mean38871.7604
Median Absolute Deviation (MAD)16755
Skewness2.846644576
Sum1166152812
Variance3546691724
MonotonicityNot monotonic
2021-11-12T19:08:27.291471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04020
 
13.4%
390207
 
0.7%
78086
 
0.3%
15078
 
0.3%
31677
 
0.3%
32656
 
0.2%
39645
 
0.1%
41636
 
0.1%
-1833
 
0.1%
240032
 
0.1%
Other values (20594)25330
84.4%
ValueCountFrequency (%)
-3396031
< 0.1%
-2090511
< 0.1%
-1509531
< 0.1%
-946251
< 0.1%
-738951
< 0.1%
-570601
< 0.1%
-514431
< 0.1%
-511831
< 0.1%
-466271
< 0.1%
-457341
< 0.1%
ValueCountFrequency (%)
9616641
< 0.1%
6999441
< 0.1%
5686381
< 0.1%
5277111
< 0.1%
5275661
< 0.1%
5149751
< 0.1%
5137981
< 0.1%
5119051
< 0.1%
5013701
< 0.1%
4991001
< 0.1%

pay_amt1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7943
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5663.5805
Minimum0
Maximum873552
Zeros5249
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-12T19:08:27.850371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2100
Q35006
95-th percentile18428.2
Maximum873552
Range873552
Interquartile range (IQR)4006

Descriptive statistics

Standard deviation16563.28035
Coefficient of variation (CV)2.924524575
Kurtosis415.2547427
Mean5663.5805
Median Absolute Deviation (MAD)1932
Skewness14.66836433
Sum169907415
Variance274342256.1
MonotonicityNot monotonic
2021-11-12T19:08:28.033351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05249
 
17.5%
20001363
 
4.5%
3000891
 
3.0%
5000698
 
2.3%
1500507
 
1.7%
4000426
 
1.4%
10000401
 
1.3%
1000365
 
1.2%
2500298
 
1.0%
6000294
 
1.0%
Other values (7933)19508
65.0%
ValueCountFrequency (%)
05249
17.5%
19
 
< 0.1%
214
 
< 0.1%
315
 
0.1%
418
 
0.1%
512
 
< 0.1%
615
 
0.1%
79
 
< 0.1%
88
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
8735521
< 0.1%
5050001
< 0.1%
4933581
< 0.1%
4239031
< 0.1%
4050161
< 0.1%
3681991
< 0.1%
3230141
< 0.1%
3048151
< 0.1%
3020001
< 0.1%
3000391
< 0.1%

pay_amt2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct7899
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5921.1635
Minimum0
Maximum1684259
Zeros5396
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-12T19:08:28.232325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1833
median2009
Q35000
95-th percentile19004.35
Maximum1684259
Range1684259
Interquartile range (IQR)4167

Descriptive statistics

Standard deviation23040.8704
Coefficient of variation (CV)3.891274139
Kurtosis1641.631911
Mean5921.1635
Median Absolute Deviation (MAD)1991
Skewness30.45381745
Sum177634905
Variance530881708.9
MonotonicityNot monotonic
2021-11-12T19:08:28.421303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05396
 
18.0%
20001290
 
4.3%
3000857
 
2.9%
5000717
 
2.4%
1000594
 
2.0%
1500521
 
1.7%
4000410
 
1.4%
10000318
 
1.1%
6000283
 
0.9%
2500251
 
0.8%
Other values (7889)19363
64.5%
ValueCountFrequency (%)
05396
18.0%
115
 
0.1%
220
 
0.1%
318
 
0.1%
411
 
< 0.1%
525
 
0.1%
68
 
< 0.1%
712
 
< 0.1%
89
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
16842591
< 0.1%
12270821
< 0.1%
12154711
< 0.1%
10245161
< 0.1%
5804641
< 0.1%
4155521
< 0.1%
4010031
< 0.1%
3881261
< 0.1%
3852281
< 0.1%
3849861
< 0.1%

pay_amt3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7518
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5225.6815
Minimum0
Maximum896040
Zeros5968
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-12T19:08:28.619312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1800
Q34505
95-th percentile17589.4
Maximum896040
Range896040
Interquartile range (IQR)4115

Descriptive statistics

Standard deviation17606.96147
Coefficient of variation (CV)3.36931393
Kurtosis564.3112295
Mean5225.6815
Median Absolute Deviation (MAD)1795
Skewness17.21663544
Sum156770445
Variance310005092.2
MonotonicityNot monotonic
2021-11-12T19:08:28.765259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05968
 
19.9%
20001285
 
4.3%
10001103
 
3.7%
3000870
 
2.9%
5000721
 
2.4%
1500490
 
1.6%
4000381
 
1.3%
10000312
 
1.0%
1200243
 
0.8%
6000241
 
0.8%
Other values (7508)18386
61.3%
ValueCountFrequency (%)
05968
19.9%
113
 
< 0.1%
219
 
0.1%
314
 
< 0.1%
415
 
0.1%
518
 
0.1%
614
 
< 0.1%
718
 
0.1%
810
 
< 0.1%
912
 
< 0.1%
ValueCountFrequency (%)
8960401
< 0.1%
8890431
< 0.1%
5082291
< 0.1%
4175881
< 0.1%
4009721
< 0.1%
3970921
< 0.1%
3804781
< 0.1%
3717181
< 0.1%
3493951
< 0.1%
3442611
< 0.1%

pay_amt4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6937
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4826.076867
Minimum0
Maximum621000
Zeros6408
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-12T19:08:28.942238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1296
median1500
Q34013.25
95-th percentile16014.95
Maximum621000
Range621000
Interquartile range (IQR)3717.25

Descriptive statistics

Standard deviation15666.15974
Coefficient of variation (CV)3.246147995
Kurtosis277.3337677
Mean4826.076867
Median Absolute Deviation (MAD)1500
Skewness12.90498482
Sum144782306
Variance245428561.1
MonotonicityNot monotonic
2021-11-12T19:08:29.090255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06408
 
21.4%
10001394
 
4.6%
20001214
 
4.0%
3000887
 
3.0%
5000810
 
2.7%
1500441
 
1.5%
4000402
 
1.3%
10000341
 
1.1%
2500259
 
0.9%
500258
 
0.9%
Other values (6927)17586
58.6%
ValueCountFrequency (%)
06408
21.4%
122
 
0.1%
222
 
0.1%
313
 
< 0.1%
420
 
0.1%
512
 
< 0.1%
616
 
0.1%
711
 
< 0.1%
87
 
< 0.1%
99
 
< 0.1%
ValueCountFrequency (%)
6210001
< 0.1%
5288971
< 0.1%
4970001
< 0.1%
4321301
< 0.1%
4000461
< 0.1%
3317881
< 0.1%
3309821
< 0.1%
3200081
< 0.1%
3130941
< 0.1%
2929621
< 0.1%

pay_amt5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6897
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4799.387633
Minimum0
Maximum426529
Zeros6703
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-12T19:08:29.254200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1252.5
median1500
Q34031.5
95-th percentile16000
Maximum426529
Range426529
Interquartile range (IQR)3779

Descriptive statistics

Standard deviation15278.30568
Coefficient of variation (CV)3.183386475
Kurtosis180.0639402
Mean4799.387633
Median Absolute Deviation (MAD)1500
Skewness11.12741705
Sum143981629
Variance233426624.4
MonotonicityNot monotonic
2021-11-12T19:08:29.395189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06703
 
22.3%
10001340
 
4.5%
20001323
 
4.4%
3000947
 
3.2%
5000814
 
2.7%
1500426
 
1.4%
4000401
 
1.3%
10000343
 
1.1%
500250
 
0.8%
6000247
 
0.8%
Other values (6887)17206
57.4%
ValueCountFrequency (%)
06703
22.3%
121
 
0.1%
213
 
< 0.1%
313
 
< 0.1%
412
 
< 0.1%
59
 
< 0.1%
67
 
< 0.1%
79
 
< 0.1%
86
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
4265291
< 0.1%
4179901
< 0.1%
3880711
< 0.1%
3792671
< 0.1%
3320001
< 0.1%
3317881
< 0.1%
3309821
< 0.1%
3268891
< 0.1%
3170771
< 0.1%
3101351
< 0.1%

pay_amt6
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6939
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5215.502567
Minimum0
Maximum528666
Zeros7173
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-11-12T19:08:29.541166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1117.75
median1500
Q34000
95-th percentile17343.8
Maximum528666
Range528666
Interquartile range (IQR)3882.25

Descriptive statistics

Standard deviation17777.46578
Coefficient of variation (CV)3.408581541
Kurtosis167.1614296
Mean5215.502567
Median Absolute Deviation (MAD)1500
Skewness10.64072733
Sum156465077
Variance316038289.4
MonotonicityNot monotonic
2021-11-12T19:08:29.695148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07173
23.9%
10001299
 
4.3%
20001295
 
4.3%
3000914
 
3.0%
5000808
 
2.7%
1500439
 
1.5%
4000411
 
1.4%
10000356
 
1.2%
500247
 
0.8%
6000220
 
0.7%
Other values (6929)16838
56.1%
ValueCountFrequency (%)
07173
23.9%
120
 
0.1%
29
 
< 0.1%
314
 
< 0.1%
412
 
< 0.1%
57
 
< 0.1%
66
 
< 0.1%
75
 
< 0.1%
86
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
5286661
< 0.1%
5271431
< 0.1%
4430011
< 0.1%
4220001
< 0.1%
4035001
< 0.1%
3770001
< 0.1%
3724951
< 0.1%
3512821
< 0.1%
3452931
< 0.1%
3080001
< 0.1%

default_payment_next_month
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.5 KiB
0
23364 
1
6636 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Length

2021-11-12T19:08:29.987113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:30.058105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring characters

ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

pay_1_-1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
24314 
1
5686 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
024314
81.0%
15686
 
19.0%

Length

2021-11-12T19:08:31.031987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:31.121977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
024314
81.0%
15686
 
19.0%

Most occurring characters

ValueCountFrequency (%)
024314
81.0%
15686
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024314
81.0%
15686
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024314
81.0%
15686
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024314
81.0%
15686
 
19.0%

pay_1_0
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
15263 
1
14737 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
015263
50.9%
114737
49.1%

Length

2021-11-12T19:08:31.340985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:31.421941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
015263
50.9%
114737
49.1%

Most occurring characters

ValueCountFrequency (%)
015263
50.9%
114737
49.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015263
50.9%
114737
49.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015263
50.9%
114737
49.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015263
50.9%
114737
49.1%

pay_1_1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
26312 
1
3688 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
026312
87.7%
13688
 
12.3%

Length

2021-11-12T19:08:31.622918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:31.700907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
026312
87.7%
13688
 
12.3%

Most occurring characters

ValueCountFrequency (%)
026312
87.7%
13688
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
026312
87.7%
13688
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
026312
87.7%
13688
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
026312
87.7%
13688
 
12.3%

pay_1_2
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
27333 
1
 
2667

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
027333
91.1%
12667
 
8.9%

Length

2021-11-12T19:08:31.891885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:31.969880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
027333
91.1%
12667
 
8.9%

Most occurring characters

ValueCountFrequency (%)
027333
91.1%
12667
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
027333
91.1%
12667
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
027333
91.1%
12667
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
027333
91.1%
12667
 
8.9%

pay_1_3
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29678 
1
 
322

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029678
98.9%
1322
 
1.1%

Length

2021-11-12T19:08:32.173882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:32.255844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029678
98.9%
1322
 
1.1%

Most occurring characters

ValueCountFrequency (%)
029678
98.9%
1322
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029678
98.9%
1322
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029678
98.9%
1322
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029678
98.9%
1322
 
1.1%

pay_1_4
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29924 
1
 
76

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Length

2021-11-12T19:08:32.461815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:32.537843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring characters

ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

pay_1_5
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29974 
1
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029974
99.9%
126
 
0.1%

Length

2021-11-12T19:08:32.742818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:32.819808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029974
99.9%
126
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029974
99.9%
126
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029974
99.9%
126
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029974
99.9%
126
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029974
99.9%
126
 
0.1%

pay_1_6
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29989 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029989
> 99.9%
111
 
< 0.1%

Length

2021-11-12T19:08:33.022783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:33.099741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029989
> 99.9%
111
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029989
> 99.9%
111
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029989
> 99.9%
111
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029989
> 99.9%
111
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029989
> 99.9%
111
 
< 0.1%

pay_1_7
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29991 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029991
> 99.9%
19
 
< 0.1%

Length

2021-11-12T19:08:33.311747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:33.387705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029991
> 99.9%
19
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029991
> 99.9%
19
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029991
> 99.9%
19
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029991
> 99.9%
19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029991
> 99.9%
19
 
< 0.1%

pay_1_8
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29981 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Length

2021-11-12T19:08:33.592680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:33.668673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

pay_2_-1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
23950 
1
6050 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
023950
79.8%
16050
 
20.2%

Length

2021-11-12T19:08:33.870647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:33.947638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
023950
79.8%
16050
 
20.2%

Most occurring characters

ValueCountFrequency (%)
023950
79.8%
16050
 
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023950
79.8%
16050
 
20.2%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
023950
79.8%
16050
 
20.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
023950
79.8%
16050
 
20.2%

pay_2_0
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1
15730 
0
14270 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
115730
52.4%
014270
47.6%

Length

2021-11-12T19:08:34.171645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:34.249603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
115730
52.4%
014270
47.6%

Most occurring characters

ValueCountFrequency (%)
115730
52.4%
014270
47.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
115730
52.4%
014270
47.6%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
115730
52.4%
014270
47.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115730
52.4%
014270
47.6%

pay_2_1
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29972 
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029972
99.9%
128
 
0.1%

Length

2021-11-12T19:08:34.463600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:34.542600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029972
99.9%
128
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029972
99.9%
128
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029972
99.9%
128
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029972
99.9%
128
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029972
99.9%
128
 
0.1%

pay_2_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
26073 
1
3927 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
026073
86.9%
13927
 
13.1%

Length

2021-11-12T19:08:34.733543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:34.810533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
026073
86.9%
13927
 
13.1%

Most occurring characters

ValueCountFrequency (%)
026073
86.9%
13927
 
13.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
026073
86.9%
13927
 
13.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
026073
86.9%
13927
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
026073
86.9%
13927
 
13.1%

pay_2_3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29674 
1
 
326

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029674
98.9%
1326
 
1.1%

Length

2021-11-12T19:08:35.014510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:35.091535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029674
98.9%
1326
 
1.1%

Most occurring characters

ValueCountFrequency (%)
029674
98.9%
1326
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029674
98.9%
1326
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029674
98.9%
1326
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029674
98.9%
1326
 
1.1%

pay_2_4
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29901 
1
 
99

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029901
99.7%
199
 
0.3%

Length

2021-11-12T19:08:35.297476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:35.379502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029901
99.7%
199
 
0.3%

Most occurring characters

ValueCountFrequency (%)
029901
99.7%
199
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029901
99.7%
199
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029901
99.7%
199
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029901
99.7%
199
 
0.3%

pay_2_5
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29975 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029975
99.9%
125
 
0.1%

Length

2021-11-12T19:08:35.587443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:35.664432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029975
99.9%
125
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029975
99.9%
125
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029975
99.9%
125
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029975
99.9%
125
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029975
99.9%
125
 
0.1%

pay_2_6
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29988 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029988
> 99.9%
112
 
< 0.1%

Length

2021-11-12T19:08:35.867441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:35.945432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029988
> 99.9%
112
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029988
> 99.9%
112
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029988
> 99.9%
112
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029988
> 99.9%
112
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029988
> 99.9%
112
 
< 0.1%

pay_2_7
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29980 
1
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029980
99.9%
120
 
0.1%

Length

2021-11-12T19:08:36.150373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:36.227363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029980
99.9%
120
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029980
99.9%
120
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029980
99.9%
120
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029980
99.9%
120
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029980
99.9%
120
 
0.1%

pay_2_8
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29999 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Length

2021-11-12T19:08:36.436373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:36.512330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

pay_3_-1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
24062 
1
5938 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
024062
80.2%
15938
 
19.8%

Length

2021-11-12T19:08:36.716306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:36.793330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
024062
80.2%
15938
 
19.8%

Most occurring characters

ValueCountFrequency (%)
024062
80.2%
15938
 
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024062
80.2%
15938
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024062
80.2%
15938
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024062
80.2%
15938
 
19.8%

pay_3_0
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1
15764 
0
14236 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
115764
52.5%
014236
47.5%

Length

2021-11-12T19:08:37.011269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:37.087260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
115764
52.5%
014236
47.5%

Most occurring characters

ValueCountFrequency (%)
115764
52.5%
014236
47.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
115764
52.5%
014236
47.5%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
115764
52.5%
014236
47.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115764
52.5%
014236
47.5%

pay_3_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29996 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Length

2021-11-12T19:08:37.701190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:37.769178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

pay_3_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
26181 
1
3819 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
026181
87.3%
13819
 
12.7%

Length

2021-11-12T19:08:37.945157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:38.019184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
026181
87.3%
13819
 
12.7%

Most occurring characters

ValueCountFrequency (%)
026181
87.3%
13819
 
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
026181
87.3%
13819
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
026181
87.3%
13819
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
026181
87.3%
13819
 
12.7%

pay_3_3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29760 
1
 
240

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029760
99.2%
1240
 
0.8%

Length

2021-11-12T19:08:38.243636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:38.323625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029760
99.2%
1240
 
0.8%

Most occurring characters

ValueCountFrequency (%)
029760
99.2%
1240
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029760
99.2%
1240
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029760
99.2%
1240
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029760
99.2%
1240
 
0.8%

pay_3_4
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29924 
1
 
76

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Length

2021-11-12T19:08:38.596627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:38.690587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring characters

ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029924
99.7%
176
 
0.3%

pay_3_5
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29979 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029979
99.9%
121
 
0.1%

Length

2021-11-12T19:08:38.984568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:39.097537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029979
99.9%
121
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029979
99.9%
121
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029979
99.9%
121
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029979
99.9%
121
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029979
99.9%
121
 
0.1%

pay_3_6
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29977 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029977
99.9%
123
 
0.1%

Length

2021-11-12T19:08:39.354503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:39.441491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029977
99.9%
123
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029977
99.9%
123
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029977
99.9%
123
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029977
99.9%
123
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029977
99.9%
123
 
0.1%

pay_3_7
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29973 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029973
99.9%
127
 
0.1%

Length

2021-11-12T19:08:39.659464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:39.739454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029973
99.9%
127
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029973
99.9%
127
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029973
99.9%
127
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029973
99.9%
127
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029973
99.9%
127
 
0.1%

pay_3_8
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29997 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029997
> 99.9%
13
 
< 0.1%

Length

2021-11-12T19:08:39.946430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:40.026420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029997
> 99.9%
13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029997
> 99.9%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029997
> 99.9%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029997
> 99.9%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029997
> 99.9%
13
 
< 0.1%

pay_4_-1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
24313 
1
5687 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
024313
81.0%
15687
 
19.0%

Length

2021-11-12T19:08:40.249393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:40.333385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
024313
81.0%
15687
 
19.0%

Most occurring characters

ValueCountFrequency (%)
024313
81.0%
15687
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024313
81.0%
15687
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024313
81.0%
15687
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024313
81.0%
15687
 
19.0%

pay_4_0
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1
16455 
0
13545 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
116455
54.9%
013545
45.1%

Length

2021-11-12T19:08:40.540360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:40.617352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
116455
54.9%
013545
45.1%

Most occurring characters

ValueCountFrequency (%)
116455
54.9%
013545
45.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
116455
54.9%
013545
45.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
116455
54.9%
013545
45.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
116455
54.9%
013545
45.1%

pay_4_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29998 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Length

2021-11-12T19:08:40.846279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:40.942312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

pay_4_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
26841 
1
3159 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
026841
89.5%
13159
 
10.5%

Length

2021-11-12T19:08:41.168240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:41.250230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
026841
89.5%
13159
 
10.5%

Most occurring characters

ValueCountFrequency (%)
026841
89.5%
13159
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
026841
89.5%
13159
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
026841
89.5%
13159
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
026841
89.5%
13159
 
10.5%

pay_4_3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29820 
1
 
180

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029820
99.4%
1180
 
0.6%

Length

2021-11-12T19:08:41.477208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:41.561193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029820
99.4%
1180
 
0.6%

Most occurring characters

ValueCountFrequency (%)
029820
99.4%
1180
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029820
99.4%
1180
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029820
99.4%
1180
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029820
99.4%
1180
 
0.6%

pay_4_4
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29931 
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029931
99.8%
169
 
0.2%

Length

2021-11-12T19:08:41.773168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:41.853157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029931
99.8%
169
 
0.2%

Most occurring characters

ValueCountFrequency (%)
029931
99.8%
169
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029931
99.8%
169
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029931
99.8%
169
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029931
99.8%
169
 
0.2%

pay_4_5
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29965 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029965
99.9%
135
 
0.1%

Length

2021-11-12T19:08:42.088140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:42.195117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029965
99.9%
135
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029965
99.9%
135
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029965
99.9%
135
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029965
99.9%
135
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029965
99.9%
135
 
0.1%

pay_4_6
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29995 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029995
> 99.9%
15
 
< 0.1%

Length

2021-11-12T19:08:42.442089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:42.545077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029995
> 99.9%
15
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029995
> 99.9%
15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029995
> 99.9%
15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029995
> 99.9%
15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029995
> 99.9%
15
 
< 0.1%

pay_4_7
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29942 
1
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Length

2021-11-12T19:08:42.828111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:42.914065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring characters

ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

pay_4_8
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29998 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Length

2021-11-12T19:08:43.141039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:43.225065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

pay_5_-1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
24461 
1
5539 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
024461
81.5%
15539
 
18.5%

Length

2021-11-12T19:08:43.470006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:43.586987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
024461
81.5%
15539
 
18.5%

Most occurring characters

ValueCountFrequency (%)
024461
81.5%
15539
 
18.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024461
81.5%
15539
 
18.5%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024461
81.5%
15539
 
18.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024461
81.5%
15539
 
18.5%

pay_5_0
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1
16947 
0
13053 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
116947
56.5%
013053
43.5%

Length

2021-11-12T19:08:43.849955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:43.936949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
116947
56.5%
013053
43.5%

Most occurring characters

ValueCountFrequency (%)
116947
56.5%
013053
43.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
116947
56.5%
013053
43.5%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
116947
56.5%
013053
43.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
116947
56.5%
013053
43.5%

pay_5_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
27374 
1
 
2626

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
027374
91.2%
12626
 
8.8%

Length

2021-11-12T19:08:44.197914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:44.282904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
027374
91.2%
12626
 
8.8%

Most occurring characters

ValueCountFrequency (%)
027374
91.2%
12626
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
027374
91.2%
12626
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
027374
91.2%
12626
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
027374
91.2%
12626
 
8.8%

pay_5_3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29822 
1
 
178

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029822
99.4%
1178
 
0.6%

Length

2021-11-12T19:08:44.585932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:44.695491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029822
99.4%
1178
 
0.6%

Most occurring characters

ValueCountFrequency (%)
029822
99.4%
1178
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029822
99.4%
1178
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029822
99.4%
1178
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029822
99.4%
1178
 
0.6%

pay_5_4
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29916 
1
 
84

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029916
99.7%
184
 
0.3%

Length

2021-11-12T19:08:44.998462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:45.101446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029916
99.7%
184
 
0.3%

Most occurring characters

ValueCountFrequency (%)
029916
99.7%
184
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029916
99.7%
184
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029916
99.7%
184
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029916
99.7%
184
 
0.3%

pay_5_5
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29983 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029983
99.9%
117
 
0.1%

Length

2021-11-12T19:08:45.372411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:45.466401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029983
99.9%
117
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029983
99.9%
117
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029983
99.9%
117
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029983
99.9%
117
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029983
99.9%
117
 
0.1%

pay_5_6
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29996 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Length

2021-11-12T19:08:45.685373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:45.767397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029996
> 99.9%
14
 
< 0.1%

pay_5_7
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29942 
1
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Length

2021-11-12T19:08:45.974340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:46.054327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring characters

ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029942
99.8%
158
 
0.2%

pay_5_8
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29999 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Length

2021-11-12T19:08:46.266339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:46.345324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029999
> 99.9%
11
 
< 0.1%

pay_6_-1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
24260 
1
5740 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
024260
80.9%
15740
 
19.1%

Length

2021-11-12T19:08:46.569269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:46.648257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
024260
80.9%
15740
 
19.1%

Most occurring characters

ValueCountFrequency (%)
024260
80.9%
15740
 
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024260
80.9%
15740
 
19.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024260
80.9%
15740
 
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024260
80.9%
15740
 
19.1%

pay_6_0
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1
16286 
0
13714 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
116286
54.3%
013714
45.7%

Length

2021-11-12T19:08:46.840235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:46.916226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
116286
54.3%
013714
45.7%

Most occurring characters

ValueCountFrequency (%)
116286
54.3%
013714
45.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
116286
54.3%
013714
45.7%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
116286
54.3%
013714
45.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
116286
54.3%
013714
45.7%

pay_6_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
27234 
1
2766 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
027234
90.8%
12766
 
9.2%

Length

2021-11-12T19:08:47.110201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:47.189227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
027234
90.8%
12766
 
9.2%

Most occurring characters

ValueCountFrequency (%)
027234
90.8%
12766
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
027234
90.8%
12766
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
027234
90.8%
12766
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
027234
90.8%
12766
 
9.2%

pay_6_3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29816 
1
 
184

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029816
99.4%
1184
 
0.6%

Length

2021-11-12T19:08:47.397169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:47.476157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029816
99.4%
1184
 
0.6%

Most occurring characters

ValueCountFrequency (%)
029816
99.4%
1184
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029816
99.4%
1184
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029816
99.4%
1184
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029816
99.4%
1184
 
0.6%

pay_6_4
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29951 
1
 
49

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029951
99.8%
149
 
0.2%

Length

2021-11-12T19:08:47.689167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:47.768123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029951
99.8%
149
 
0.2%

Most occurring characters

ValueCountFrequency (%)
029951
99.8%
149
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029951
99.8%
149
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029951
99.8%
149
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029951
99.8%
149
 
0.2%

pay_6_5
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29987 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029987
> 99.9%
113
 
< 0.1%

Length

2021-11-12T19:08:47.972106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:48.052093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029987
> 99.9%
113
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029987
> 99.9%
113
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029987
> 99.9%
113
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029987
> 99.9%
113
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029987
> 99.9%
113
 
< 0.1%

pay_6_6
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29981 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Length

2021-11-12T19:08:48.257069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:48.335054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring characters

ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029981
99.9%
119
 
0.1%

pay_6_7
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29954 
1
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029954
99.8%
146
 
0.2%

Length

2021-11-12T19:08:48.545067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:48.628055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029954
99.8%
146
 
0.2%

Most occurring characters

ValueCountFrequency (%)
029954
99.8%
146
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029954
99.8%
146
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029954
99.8%
146
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029954
99.8%
146
 
0.2%

pay_6_8
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29998 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Length

2021-11-12T19:08:48.831027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:48.908025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029998
> 99.9%
12
 
< 0.1%

female
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1
18112 
0
11888 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
118112
60.4%
011888
39.6%

Length

2021-11-12T19:08:49.111964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:49.188951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
118112
60.4%
011888
39.6%

Most occurring characters

ValueCountFrequency (%)
118112
60.4%
011888
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
118112
60.4%
011888
39.6%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
118112
60.4%
011888
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
118112
60.4%
011888
39.6%

education_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
19415 
1
10585 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019415
64.7%
110585
35.3%

Length

2021-11-12T19:08:49.379933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:49.460954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
019415
64.7%
110585
35.3%

Most occurring characters

ValueCountFrequency (%)
019415
64.7%
110585
35.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
019415
64.7%
110585
35.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
019415
64.7%
110585
35.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
019415
64.7%
110585
35.3%

education_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
15970 
1
14030 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
015970
53.2%
114030
46.8%

Length

2021-11-12T19:08:49.688489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:49.774480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
015970
53.2%
114030
46.8%

Most occurring characters

ValueCountFrequency (%)
015970
53.2%
114030
46.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015970
53.2%
114030
46.8%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015970
53.2%
114030
46.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015970
53.2%
114030
46.8%

education_3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
25083 
1
4917 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
025083
83.6%
14917
 
16.4%

Length

2021-11-12T19:08:49.998488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:50.108557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
025083
83.6%
14917
 
16.4%

Most occurring characters

ValueCountFrequency (%)
025083
83.6%
14917
 
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025083
83.6%
14917
 
16.4%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
025083
83.6%
14917
 
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025083
83.6%
14917
 
16.4%

education_4
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29546 
1
 
454

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029546
98.5%
1454
 
1.5%

Length

2021-11-12T19:08:50.384950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:50.475939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029546
98.5%
1454
 
1.5%

Most occurring characters

ValueCountFrequency (%)
029546
98.5%
1454
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029546
98.5%
1454
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029546
98.5%
1454
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029546
98.5%
1454
 
1.5%

education_5
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
30000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
030000
100.0%

Length

2021-11-12T19:08:51.243876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:51.317833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
030000
100.0%

Most occurring characters

ValueCountFrequency (%)
030000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
030000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
030000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
030000
100.0%

education_6
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
30000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
030000
100.0%

Length

2021-11-12T19:08:51.505846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:51.580835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
030000
100.0%

Most occurring characters

ValueCountFrequency (%)
030000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
030000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
030000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
030000
100.0%

marriage_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
16341 
1
13659 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
016341
54.5%
113659
45.5%

Length

2021-11-12T19:08:51.800786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:51.903767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
016341
54.5%
113659
45.5%

Most occurring characters

ValueCountFrequency (%)
016341
54.5%
113659
45.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016341
54.5%
113659
45.5%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016341
54.5%
113659
45.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016341
54.5%
113659
45.5%

marriage_2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
1
15964 
0
14036 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
115964
53.2%
014036
46.8%

Length

2021-11-12T19:08:52.136736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:52.217759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
115964
53.2%
014036
46.8%

Most occurring characters

ValueCountFrequency (%)
115964
53.2%
014036
46.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
115964
53.2%
014036
46.8%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
115964
53.2%
014036
46.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
115964
53.2%
014036
46.8%

marriage_3
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
29623 
1
 
377

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029623
98.7%
1377
 
1.3%

Length

2021-11-12T19:08:52.477830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-12T19:08:52.583918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
029623
98.7%
1377
 
1.3%

Most occurring characters

ValueCountFrequency (%)
029623
98.7%
1377
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029623
98.7%
1377
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029623
98.7%
1377
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029623
98.7%
1377
 
1.3%

Interactions

2021-11-12T19:07:45.302121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:45.465069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:45.636076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:45.804028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:45.971005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:46.122988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:46.279969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:46.430983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:46.568932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:46.717916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:46.853900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:47.002879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:47.243853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:47.384874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:47.520821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:47.660801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:47.799696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:47.935682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:48.073715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:48.214649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:48.349632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:48.498646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:48.633636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:48.767579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:48.910597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:49.042546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:49.193530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:49.325514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:49.465496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:49.601483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:49.737478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:49.877914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:50.015894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:50.155913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:50.298025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:50.437005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:50.587987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:50.724005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:50.856956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:51.002941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:51.133926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:51.279903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:51.411891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:51.552872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:51.688854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:51.830842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:52.096809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:52.236789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:52.378157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:52.525104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:52.669087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:52.818101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:52.960052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:53.098036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:53.245057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:53.379035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:53.529988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:53.672967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:53.817948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:53.953936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:54.094945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:54.237897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:54.378882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:54.514864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:54.658887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:54.801868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:54.949849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:55.089833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:55.220805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:55.364816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:55.497773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:55.677751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:55.822769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:55.971754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:56.106361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:56.246346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:56.406323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:56.557876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:56.710529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:56.868555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:57.023006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:57.186987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:57.352968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:57.504981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:57.668040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:57.815120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:57.975110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:58.271072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:58.423059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:58.563977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:58.710973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:58.847971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:58.987982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:59.126967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:59.268964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:59.413999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:59.563976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:59.702975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:59.834972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:07:59.981966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:00.129951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:00.278946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:00.411753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:00.554739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:00.686722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:00.826703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:00.961221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:01.099727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:01.233727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:01.370721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:01.505709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:01.646721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:01.773673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:01.895708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:02.034735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:02.160599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:02.297633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:02.424628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:02.558611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:02.680614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:02.806628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:02.954593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:03.102485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:03.257528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:03.412550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:03.580257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:03.744233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:03.903216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:04.059200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:04.251172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:04.440191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:04.641131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:04.836104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:05.028086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:05.222063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:05.422038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:05.565051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:05.704000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:06.019961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:06.148944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:06.274963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:06.412912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:06.547898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:06.670916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:06.806561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:06.931544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:07.078527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:07.203511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:07.334495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:07.463481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:07.593464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:07.745447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:07.897468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:08.047444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:08.198392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:08.343418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:08.498775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:08.654793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:08.797773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:08.955720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:09.099704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:09.261687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:09.405670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:09.564646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:09.710630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:09.859279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:09.998266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:10.131248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:10.262608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:10.398921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:10.529906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:10.673888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:10.803872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:10.927855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:11.063838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:11.186827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:11.322811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:11.446792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:11.580778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:11.709794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:11.838744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:11.980757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:12.123713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:12.262694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:12.408711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:12.554190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:12.708172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:12.849154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:12.993172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:13.143118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:13.280102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:13.428121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:13.576066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:13.729049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:13.867032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:14.009016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:14.141000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:14.294997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:14.471974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:14.653951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:14.795935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:15.238904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:15.414896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:15.591878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:15.775820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:15.952797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:16.134776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:16.295786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:16.457737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:16.590722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:16.723702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:16.877687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:17.076664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:17.239646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:17.445652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:17.655598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:17.846570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:17.999552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:18.173570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:18.353508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:18.535490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:18.744466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:18.938440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:19.140416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-11-12T19:08:19.320405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-11-12T19:08:52.886532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-12T19:08:55.752189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-12T19:08:58.595845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-12T19:09:01.223530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-11-12T19:09:03.715234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-12T19:08:20.398266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idlimit_balagebill_amt1bill_amt2bill_amt3bill_amt4bill_amt5bill_amt6pay_amt1pay_amt2pay_amt3pay_amt4pay_amt5pay_amt6default_payment_next_monthpay_1_-1pay_1_0pay_1_1pay_1_2pay_1_3pay_1_4pay_1_5pay_1_6pay_1_7pay_1_8pay_2_-1pay_2_0pay_2_1pay_2_2pay_2_3pay_2_4pay_2_5pay_2_6pay_2_7pay_2_8pay_3_-1pay_3_0pay_3_1pay_3_2pay_3_3pay_3_4pay_3_5pay_3_6pay_3_7pay_3_8pay_4_-1pay_4_0pay_4_1pay_4_2pay_4_3pay_4_4pay_4_5pay_4_6pay_4_7pay_4_8pay_5_-1pay_5_0pay_5_2pay_5_3pay_5_4pay_5_5pay_5_6pay_5_7pay_5_8pay_6_-1pay_6_0pay_6_2pay_6_3pay_6_4pay_6_5pay_6_6pay_6_7pay_6_8femaleeducation_1education_2education_3education_4education_5education_6marriage_1marriage_2marriage_3
0120000.0243913.03102.0689.00.00.00.00.0689.00.00.00.00.0100010000000001000000100000000010000000000000000000000000001010000100
12120000.0262682.01725.02682.03272.03455.03261.00.01000.01000.01000.00.02000.0110000000000001000000010000000001000000000100000000010000001010000010
2390000.03429239.014027.013559.014331.014948.015549.01518.01500.01000.01000.01000.05000.0001000000000100000000010000000001000000000100000000100000001010000010
3450000.03746990.048233.049291.028314.028959.029547.02000.02019.01200.01100.01069.01000.0001000000000100000000010000000001000000000100000000100000001010000100
4550000.0578617.05670.035835.020940.019146.019131.02000.036681.010000.09000.0689.0679.0010000000000100000000100000000001000000000100000000100000000010000100
5650000.03764400.057069.057608.019394.019619.020024.02500.01815.0657.01000.01000.0800.0001000000000100000000010000000001000000000100000000100000000100000010
67500000.029367965.0412023.0445007.0542653.0483003.0473944.055000.040000.038000.020239.013750.013770.0001000000000100000000010000000001000000000100000000100000000100000010
78100000.02311876.0380.0601.0221.0-159.0567.0380.0601.00.0581.01687.01542.0001000000001000000000100000000001000000000100000001000000001010000010
89140000.02811285.014096.012108.012211.011793.03719.03329.00.0432.01000.01000.01000.0001000000000100000000000100000001000000000100000000100000001001000100
91020000.0350.00.00.00.013007.013912.00.00.00.013007.01122.00.0000000000000000000000000000000000000000001000000001000000000001000010

Last rows

idlimit_balagebill_amt1bill_amt2bill_amt3bill_amt4bill_amt5bill_amt6pay_amt1pay_amt2pay_amt3pay_amt4pay_amt5pay_amt6default_payment_next_monthpay_1_-1pay_1_0pay_1_1pay_1_2pay_1_3pay_1_4pay_1_5pay_1_6pay_1_7pay_1_8pay_2_-1pay_2_0pay_2_1pay_2_2pay_2_3pay_2_4pay_2_5pay_2_6pay_2_7pay_2_8pay_3_-1pay_3_0pay_3_1pay_3_2pay_3_3pay_3_4pay_3_5pay_3_6pay_3_7pay_3_8pay_4_-1pay_4_0pay_4_1pay_4_2pay_4_3pay_4_4pay_4_5pay_4_6pay_4_7pay_4_8pay_5_-1pay_5_0pay_5_2pay_5_3pay_5_4pay_5_5pay_5_6pay_5_7pay_5_8pay_6_-1pay_6_0pay_6_2pay_6_3pay_6_4pay_6_5pay_6_6pay_6_7pay_6_8femaleeducation_1education_2education_3education_4education_5education_6marriage_1marriage_2marriage_3
2999029991140000.041138325.0137142.0139110.0138262.049675.046121.06000.07000.04228.01505.02000.02000.0001000000000100000000010000000001000000000100000000100000000010000100
2999129992210000.0342500.02500.02500.02500.02500.02500.00.00.00.00.00.00.0100001000000001000000000100000000010000000010000000010000000010000100
299922999310000.0438802.010400.00.00.00.00.02000.00.00.00.00.00.0001000000000100000000010000000000000000000000000000000000000001000100
2999329994100000.0383042.01427.0102996.070626.069473.055004.02000.0111784.04000.03000.02000.02000.0001000000001000000000100000000001000000000100000000100000000100000010
299942999580000.03472557.077708.079384.077519.082607.081158.07000.03500.00.07000.00.04000.0100010000000001000000000100000000010000000010000000010000000010000010
2999529996220000.039188948.0192815.0208365.088004.031237.015980.08500.020000.05003.03047.05000.01000.0001000000000100000000010000000001000000000100000000100000000001000100
2999629997150000.0431683.01828.03502.08979.05190.00.01837.03526.08998.0129.00.00.0010000000001000000000100000000010000000000100000000100000000001000010
299972999830000.0373565.03356.02758.020878.020582.019357.00.00.022000.04200.02000.03100.0100000100000000100000000100000010000000000100000000100000000010000010
299982999980000.041-1645.078379.076304.052774.011855.048944.085900.03409.01178.01926.052964.01804.0100100000001000000000010000000001000000000100000001000000000001000100
299993000050000.04647929.048905.049764.036535.032428.015313.02078.01800.01430.01000.01000.01000.0101000000000100000000010000000001000000000100000000100000000010000100